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Record W2975790188 · doi:10.7202/1064503ar

“Some Account of an Extraordinary Traveller”: Using Virtual Tours to Access Remote Heritage Sites of Inuit Cultural Knowledge

2019· article· en· W2975790188 on OpenAlexaffvenueabout
Peter Dawson, Cecilia Porter, Denis Gadbois, Darren Keith, Colleen Hughes, Luke Suluk

Bibliographic record

VenueÉtudes/Inuit/Studies · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPanoramaIndigenousCultural heritageVariety (cybernetics)World heritageGeographyValue (mathematics)Visual artsIdentity (music)TourismHistoryArchaeologyComputer scienceAestheticsArt

Abstract

fetched live from OpenAlex

The use of panoramic images to transport viewers to remote geographic locations can be traced back to the panorama theatres of nineteenth-century Victorian London. More recently, Google’s World Wonders Project has utilized 360-degree panospheres to capture some of the world’s most famous heritage sites. Using arrows that demarcate a defined path of movement, users can virtually tour these sites by “jumping” from one panosphere to the next. Arvia’juaq National Historic site is located near the community of Arviat. Although the heritage value of the site is highly significant, Arvia’juaq sees few national and international visitors because of its remote location. For a variety of reasons, some local Inuit also find it difficult to regularly visit the site even though it is an important source of cultural identity. In this paper, we explore how panospheres can be used to create interactive virtual tours of heritage sites like Arvia’juaq. Although there are some caveats, we argue that virtual reality (VR) tours are potentially powerful tools for connecting people to heritage sites that might otherwise be inaccessible. This has important implications for raising awareness of polar heritage and its significance to Indigenous people, as well as national and international audiences.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.133
GPT teacher head0.354
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2019
Admission routes3
Has abstractyes

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